# -------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
#
# -------------------------------------------------------------

# Autogenerated By   : src/main/python/generator/generator.py
# Autogenerated From : scripts/builtin/normalize.dml

from typing import Dict, Iterable

from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar
from systemds.utils.consts import VALID_INPUT_TYPES


def normalize(X: Matrix):
    """
     Min-max normalization (a.k.a. min-max scaling) to range [0,1]. For matrices 
     of positive values, this normalization preserves the input sparsity.
    
     .. code-block:: python
    
       >>> import numpy as np
       >>> from systemds.context import SystemDSContext
       >>> from systemds.operator.algorithm import normalize
       >>> 
       >>> with SystemDSContext() as sds:
       ...     X = sds.from_numpy(np.array([[1, 2], [3, 4]]))
       ...     Y, cmin, cmax = normalize(X).compute()
       ...     print(Y)
       [[0. 0.]
        [1. 1.]]
    
    
    
    
    :param X: Input feature matrix of shape n-by-m
    :return: Modified output feature matrix of shape n-by-m
    :return: Column minima of shape 1-by-m
    :return: Column maxima of shape 1-by-m
    """

    params_dict = {'X': X}
    
    vX_0 = Matrix(X.sds_context, '')
    vX_1 = Matrix(X.sds_context, '')
    vX_2 = Matrix(X.sds_context, '')
    output_nodes = [vX_0, vX_1, vX_2, ]

    op = MultiReturn(X.sds_context, 'normalize', output_nodes, named_input_nodes=params_dict)

    vX_0._unnamed_input_nodes = [op]
    vX_1._unnamed_input_nodes = [op]
    vX_2._unnamed_input_nodes = [op]

    return op
